department of aerospace science and technology · ‣part of the n/s arm of the northern cross...

Department of Aerospace Science and

Technology

Space Mission Engineering Lab

Outline

1.Personnel

2.Collaborations

3.Research Activities

4.Experimental Facilities

Research activities

Space Missions Engineering laboratory

Teaching Staff: Franco Bernelli Zazzera, James Biggs, Camilla

Colombo, Pierluigi Di Lizia, Michèle Lavagna , Paolo Lunghi,

Mauro Massari, Francesco Topputo

Other: Amalia Ercoli Finzi (honorary professor)

• Public Insititutions

• ASI – Italian Space Agency

• ESA – European Space Agency

• CNES – French Space Agency

• DLR - German Space Agency

• Industries

• Thales-Alenia Space I

• Thales-Alenia Space F

• OHB System

• Kaiser-Traede

• OHB CGS

• QinetiQ

• Leonardo SES

• GMV Space Systems

• Elecnor Deimos Space

• SciSys

• CESI

• D-Orbit

• Vega-Telespazio

Collaborations

• Universities\Research Centres

• MIT – Massachusetts Institute of

Technology

• MSU – Michigan State University, US

• TAMU – Texas A&M University, US

• PU – Princeton University, US

• VirginiaTech, US

• NPS Monterey, US

• Purdue University, La Fayette, Indiana

• University of Arizona, US

• University of California, Irvine, US

• Southampton University, UK

• University of Surrey, UK

• University of Florida, US

• Tohoku University, Sendai, Japan

• Beihang, China

• National University of Defense

Technology, Changsha, China

• Shanghai Aerospace Control Engineering

Institute, China

• Universita di Milano Bicocca

• Universita Federico II-Napoli

• Università La Sapienza-Roma

• Politecnico di Torino

• Università di Bologna

• CIRA

• CNR

• INAF

Collaborations

ModellingSimulation

Analysis

&

Design

Global

Optimization

Robust\Optimal

Control

Muldisciplinary

optimization

Uncertainties

propagation

Soft Computing

Differential

Algebra

Space

Environment

Multi-body

Dynamics

Mission Analysis

Space Science & Technology

Relative

Dynamics

Local

Optimization

Dynamics

&

Control Adaptive

Control

Atmospheric

Maneuvering

Mission Analysis

and

Interplanetary

trajectories

Planetary Orbits

Precise Station

Keeping, SAA, contact

windows, etc

Mission Analysis

Robust orbits

propagation

Entry Descent

&LandingFormation Flying

Control

Irregular natural

bodies geopotential

modelling

N-Body models

trajectories

Orbit determination

under uncertainties

Space Situational Awareness – NEO and Debris

Algorithms implementation

• Orbit determination based on

different sensors/sensors architecture

• Accurate propagation of uncertainties

• Conjunctions identification

• Collision probability computation

System trade-off and design

• Definition and critical review of

requirements for sensors/sensors

architecture implementation

• Support in the design of sensors

and network architecture (e.g. sensor

type, sensor performances,....)

• System simulation and assessment of system performances

Formation Flying

Absolute and relative trajectories design

Station-keeping for both impulsive and low-thrust maneuvers

Reconfiguration maneuvers with optimal feedback control

4 s/c reconfiguration 4 s/c reconfiguration (collision

avoidance)

Atmospheric Phases

Multidisciplinary and Multiobjective optimizationMultiobjective optimization of AGA

Multidisciplinary optimization of EDL

phase

Aero Gravity Assist and Aerocapture maneuvers

trajectory, control, and vehicle shape optimization

EDL phases:

guidance, shape, thermal

protection, sequence timing

Orbit Design - Interplanetary

Global optimization

– Deterministic optimization of

MGA+DSM transfers (gravity assist

space pruning based on Differential

Algebra)

Objective function (Earth-Mars

transfer)

Low-thrust transfer (EMMJ)

Local optimization

– Optimization of MGA+Low-Thrust

Goal: Validate GNC landing, vision based, for planetary and low gravity bodiesdescent

Features: precise landing; retargeting landing (adaptive guidance) site upon hazard infos,on board closed loop

Automated Guidance, Navigation and Control

for Spacecraft Landing

In red: building blocks under development @ Politecnico di Milano DAER

Adaptive Guidance

Landing trajectory computation: Constrained Two Points Boundary Value Problem.

Requirements:

• Fast computation (real-time trajectory update);

• Fuel optimal (in case more than one retargeting is required during the descent).

Semi-analytical, optimized landing trajectory:

• Polynomial formulation satisfies boundary constraints;

• Additional parameters can be tuned to optimize the landing trajectory, according to

path constraints;

• Light direct optimization algorithms (eg. Compass Search);

• Tested through Monte Carlo simulation for both fast (planetary) and slow (asteroids)

dynamics.

On going activity

Closed-loop simulation campaign to assess the capability of hazard avoidance, navigation

and guidance to work together)





Moon landing adaptive guidance MC test case

• Diversion ordered at altitude 2000m, 600m (1σ);• dispersion at touchdown <16m (3σ). Navigation errors included

Asteroid landing adaptive guidance MC test case

Mission Analysis - Earth

Mission design for EO missions including

– Orbit selection, launcher selection, launch windows

– Coverage, Access, and Light condition analysis

– Orbital simulation and environmental analysis

– ∆V Budget and station keeping strategy

– Optimal control theory applied to station keeping

SZA vs day of the year Projection of Earth orbits

Dynamics and Control of Non-Keplerian Orbits

16

GoalLow Energy Interplanetary Transfers (LEIT)

Solution

The R3BP to model the space dynamics

Transit orbits of the Sun-planet systems

Low energy planet approach through small necks

Invariant manifolds as key to design LEIT

4BP split in two R3BP

Intersection of the invariant maifolds in the solution

space

The restricted three-body approximation as

refinement of the patched-conics method for LEIT

design

SMALL BODIES GRAVITY FIELD MODELS

Accurate mass distribution/gravity field

model of the body

Effective trajectory design around a low gravity bodyGravitational potential

Acceleration model


Asteroid gravity field models

GRAVITATIONAL AGGREGATE

Used to simulate aggregation process. Very accurate for

certain classes of asteroids. It models internal voids

ELLIPSOIDSimple model, it works well to

design trajectories far from asteroid surface

POLYHEDRONVery accurate model, it works

well in the whole domain, including the design of

landing/lift off trajectories.Customizable on the specific

asteroid


Example: Ellipsoid vs Polyhedron

latitu

de

[d

eg

]la

titu

de

[d

eg

]

longitude [deg]

longitude [deg]

a [m/s2]

a [m/s2]

a [m/s2]

a [m/s2]

[km]

[km]

Gravity field is studied and compared between different

modelsTo be validated through data on subsurface

composition , material distribution and structure


Example: N-body aggregation sequence

Gravitational aggregate

1 2 3

4 5 6

Asteroids and binary systems formation\distruption and rotational dynamics understanding

To be validated through data on subsurface

composition , material distribution and structure

Space Debris Problem

‣ Debris: any defunct object in Earth orbit

• Size ranging from mm to a few meters

• More than 22,000 objects larger than 10

cm currently tracked

• Explosions and collisions boost the

number of fragments and could trigger

Kessler’s syndrome

Explosion

Collision

‣ Crucial: Monitoring, tracking, and

predicting trajectories of space debris

• Identification of orbital collision for

avoidance maneuver planning

• Re-entry forecast for larger objects

• Detection of in-orbit fragmentations

and collisions between uncontrolled

objects

‣ Highest concentration: LEO and GEO Credits: ESA

Orbit Determination with Multibeam Radars

E/W arm: single antenna

(564 m x 35 m)

N/S arm: array of 64 antennas

(640 m x 23.5 m)

‣ Part of the N/S arm of the Northern Cross Radiotelescope (Medicina,

Bologna, Italy) has been refurbished to enable radar multibeaming

• 32 receivers mounted on 8 antennas

• Resulting FoV = 38 deg2 (Dec: 5.7 deg, AR: 6.6 deg)

• Back end processing allows the FoV to be divided into 32 beams

Main advantage: beams illumination sequence supplies declination and right

ascension profiles during transit in addition to range and Doppler shift


‣ Part of the N/S arm of the Northern Cross Radiotelescope (Medicina,

Bologna, Italy) has been refurbished to enable radar multibeaming

• 32 receivers mounted on 8 antennas

• Resulting FoV = 38 deg2 (Dec: 5.7 deg, AR: 6.6 deg)

• Back end processing allows the FoV to be divided into 32 beams

Main advantage: beams illumination sequence supplies declination and right

ascension profiles during transit in addition to range and Doppler shift

BEST-2


BES

T-2

Step 1 Step 2

‣ Two-step algorithm developed for orbit determination:

• Step 1: Estimation of right ascension and declination profiles from SNR profiles

• Step 2: Estimation of object position and velocity by matching orbital trajectory

with range measurements and observables profiles

‣ First numerical tests suggests that orbit determination could be performed

with a single transit in the FoV (good RCS estimate necessary)

‣ The software has been extended to manage both radar and optical inputs

‣ Software currently under developments for INAF under ASI contract

Correlating Observations

‣ One optical/radar observation of an unknown object is not sufficient to

accomplish an initial orbit determination

‣ However, an admissible region (AR) can be identified from one

observation based on energetic constraints

‣ The AR must be propagated forward to schedule additional observations of

the same object

Intensive Monte Carlo

simulations

‣ Moreover, assume two ARs are

available from two different

observations

• How can we check if the two

observations are correlated?

AR 1AR 2

T11T21

Same object?

1

• Main drawback: the region is usually

large and the dynamics is nonlinear

Correlating Observations

AR 1AR 2

T1 T21

AR

2

Tcommon1

‣ Main Idea: use differential algebra and automatic domain splitting to

propagate large admissible regions

• Differential algebra is used to replace pointwise numerical integrations with the

fast evaluation of polynomials

• Automatic domain splitting is added to improve accuracy of polynomials for very

large ARs and/or long term propagations

‣ Main advantages

• The ARs can be quickly propagated to schedule new observations

• ARs from different observations can be propagated to a common epoch and

their intersection can be checked to correlate the observations

‣ Under study for Air Force R.L. in partnership with Universidad de La Rioja

Ballistic captureFor Mars missions

27

−8 −6 −4 −2 0 2 4

x 10−3

−7

−6

−5

−4

−3

−2

−1

0

1

2

3

x 10−3

x (adim.)

y (

adim

.)

Ballistic capture transfers to Mars:

•Can achieve substantial savings in capture Δv (~20%)

•Is safer than hyperbolic approach, allows multiple

insertion options

•Involve much more flexible launch windows

Space SHIPSpace Systems with Hybrid Propulsion

28

Attempt to combine the benefits of

• Chemical propulsion

• Electric propulsion

Can be applied in missions to

• Moon

• Mars

• NEOs

Preliminary solutionCPU model

Thruster model Refined solution

• Overcome the binary trade space

• Fully-chemical and fully-electrical

configurations viewed as special hybrid

solutions

• Challenge is to assess their implication

at system level

Space SHIPSpace Systems with Hybrid Propulsion

29

• Propulsion not expressive innovative, their combined

utilization brings novelty

• Hybrid system can ease operation (no GA)

• High power needed during the transfer, but

• Useful for high-power payloads

• Which subsystem do the SA belong to?

Preliminary results show:

• Considerable savings wrt fully chemical solutions

• Shorter transfer times wrt fully electric solutions

• Possible standardization for Moon, Mars, NEOs mission

FSM: Final spacecraft mass

UMAT: Useful mass at target(Results from ITT 6791)

Miniaturization

Guidance and control of resource limited

spacecraft

Developing guidance and contol algorithms for highly constrained but highly

responsive spacecraft

Computationally efficient for on-board implementation.

Guidance methods that incorporate 6 dof obstacle avoidance and precision docking.

Control methods for the deployment in uncertain environments.

Fault-tolerant control methods.

Algorithms for efficient motions using novel propulsion – solar sail, pulse-plasma thrusters

More responsive, cheaper.

Less reliable, resource limited.

Tools

Nonlinear Dynamical systems and Control theory.

Differential geometry for global motion planning on non-Euclidean spaces.

Simulation.

Test-bed experimentation.

Dynamics in highly inhomogeneous gravity fields

Investigations of the dynamical environments about Asteroids and the moons of Mars

Analytical Perturbative methods for global orbit identification.

Numerical continuation to compute families of orbits.

Nonlinear and linear stability analysis of orbits.

Station-keeping in the presence of uncertainties and constraints.

Modelling and simulation of spacecraft

dynamics

MODELICA: a language for physical systems modelling.

Main features:

– Based on equations rather than assignments;– Acausal models;– Can be used to model multidomain systems;– Object-oriented language (based on classes);– Component interaction described by connectors;

Modelica Spacecraft Dynamics Library:

– Based on the DLR Modelica Multibody Library;– A unique environment for the entire AOCS design cycle;– Possibility of multidomain simulation;– Easy management of spacecraft configurations; – Rapid prototyping of AOCS subsystems.

Generic spacecraft simulator (Modelica/Dymola)

Sensors block

Spacecraft model

Spacecraft dynamics

Actuators block

Data sheets

Attitude determination:

estimation and filtering

Background:

– development of the EKFs for the Italian missions MITA and AGILE.

– Development of a novel algorithm for

star identification; implementation of

on-board code for star sensor-based

attitude determination.

Recent activities

– Study of UKFs for attitude estimation

– Predictive filters for attitude estimation

– Globally convergent filters for magnetic attitude estimation

– High accuracy attitude and rate estimation using magneto-hydrodynamic sensors

Star cameraStar

Identification

Attitude

Determination

Star Catalog

Star sensor

Attitude

Angular

rate

Starsensor

0 2000 4000 6000 8000 10000 12000-0.04

-0.03

-0.02

-0.01

0

0.01

0.02theta

time [s]

Roll

angle

[ra

d]

0 2000 4000 6000 8000 10000 12000-0.02

-0.01

0

0.01

0.02psi

time [s]

Pitch a

ngle

[ra

d}

Attitude control:

full and partial magnetic actuation

Problem:

attitude control for satellites using magnetic coils as primary or sole actuators

Torque generation mechanism: interaction between current-driven coils and geomagnetic field. Magnetic field periodic along orbit LTP linearised attitude dynamics.

Critical issues:

Magnetic actuators are intrinsically time-varyingThe magnetically actuated spacecraft is not instantaneously controllable

Magnetic attitude control

Nominal phase: tools for numerical optimisation of linear state feedback controllers

Acquisition phase: methods for convergence analysis of acquisition transients

Partially magnetic control: design tools for coils+ thrusters configuration

Robust analysis and synthesis: methods and tools for robust design and worst-case analysis for linear magnetic ACS loops.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.9997

0.9998

0.9999

1

q1

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.01

0

0.01

0.02

q2

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.02

0

0.02

q3

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.01

0

0.01

q4

Orbits

Magnetic attitude control

Approximate solution to nonlinear optimal control

SDC factorization

Space ShepherdMonitoring refugees in the Mediterranean Sea

38

•Loss of human lives

•Emergency conditions

•Raising lands

•Critical shelter

Motivations

• Land systems used

• Local coverage

• Deployment of assets

• Territorial waters

State of the art

To use satellites already operating on a regular basis to monitor, detect, track

immigration flows in the Mediterranean Sea and support rescue and landing

The idea

• Innovative approach

• Global coverage

• Dual use (“zero” cost)

•Multidisciplinary nature

Originality

Context

2014: 170k migrants (460/day)

Jan-Apr 2014/Jan-Apr 2015:

+2900% casualties

2015: 240k migrants (estimated)


39

•To remotely monitor portions of sea

•To detect migrant vessels

•To infer the situational awareness

•To track the vessels and raise flags

•To support the rescue operations

•To improve logistics of landings

•To develop a simulation framework

•To integrate SAR/optical imageries

GoalsMethodology

Land radar range 100

km (over-estimated)

Sea radar range 60

km (over-estimated)

Ship-to-ship visual range

20 km (over-estimated)


40









GoalsMethodology


km (over-estimated)

Sea radar range 60

km (over-estimated)




41









GoalsMethodology


km (over-estimated)

Sea radar range 60

km (over-estimated)



4 COSMO-SkyMed,

1 day, resolution 30 m,

swath 100 km


42









GoalsMethodology


km (over-estimated)

Sea radar range 60

km (over-estimated)




43

TLE

SGP4

Sensor geometry

Ground stations

Contact windows

Kinematics model

Initial uncertainty

Uncertainty propagation

Longitude10 15 20 25 30 35

La

titu

de

30

32

34

36

38

40

42

C28

C39

??-5

C56

C62

C63

C7

C17

C29

C42

C51

C58

C61

C13

C26C17

C21

C44

C46

C56C40

C61

??-8

COSMO-SkyMed 2

COSMO-SkyMed 3

COSMO-SkyMed 4

COSMO-SkyMed 1

SAR strips @ sea

Optical images @ ports

•Monitoring

•Tracking

•Support to S&R

• Warnings

• Info on targets

• Info on nearby vessels

• Feedback

Type of ship Length [m] Number of people carried

(from sources)

Speed range

[knots]

Source

Rust-bucket

Old african fishing boats

15-20 100-200 2-10* www.marina.difesa.it

www.lastampa.it

Fishing motorboats 25-30 200-250 (even 350)

2-10 * www.ilsecoloxix.it www.ilfattoquotidiano.it

Rubber dinghies 10-15 100 5-10* www.marina.difesa.it

Big fishing

motorboats or small cargo boats (Mother

ships)

≥ 30 >200 (even

500)

10-15

(presumed)

www.gdf.gov.it

www.ilsole24ore.com

Cargo or merchant ships (for the whole

crossing)

30-70 ≥ 500 200-800

10-20 * www.palermo.repubblica.it www.repubblica.it

Sail boats 10-20 Variable ( > 40) < 10

(presumed)

www.gdf.gov.it

www.europaquotidiano.it

www.corriere.it

Analisi del sistema AIS Capire il funzionamento del sistema AIS (Automatic Identification System) è di fondamentale importanza ai

fini del progetto in quanto le immagini satellitari devono essere depurate delle imbarcazioni autorizzate, che trasmettono la propria posizione attraverso il sistema AIS. Per questo motivo è stata condotta un’analisi sul

funzionamento di tale sistema. I risultati sono descritti nel report 1 “project meeting #1” (allegato) e sono brevemente riassunti di seguito:

· In Italia il sistema è obbligatorio sulla quasi totalità delle imbarcazioni. Nello specifico, le imbarcazioni autorizzate che presumibilmente saranno presenti nelle immagini satellitari sarano tutte

dotate di sistema AIS;

· Sebbene esista una sperimentazione sull’uso dei satelliti, il sistema AIS non utilizza una

trasmissione satellitare, bensì una tecnologia peer-to-peer, per cui è possibile trasmettere la propria posizione solo se si è in vista di altre imbarcazioni che possano fungere da “ponte” per il segnale.

AIS-Problem1: Missing Ships

19/03/15 Space Shepherd

Esempio di posizionamento con sistema AIS

Requisiti, architettura e implementazione del simulatore Lo sviluppo del simulatore è stato messo in atto attraverso i seguenti passi: 1) Creazione di un database di

satelliti candidati (allegato), 2) formulazione dei requisiti, 3) definizione dell’architettura, 4) validazione

preliminare. Per quanto riguarda i requisiti, il simulatore opererà in tre diverse modalità, a seconda del


44

Coverage Mean Time

Mean Cycle Time

Average Cycle Response Time

Mean time between two

acquisitions of the same

target

Mean to:1) uplink the

instructions, 2) take the

image, 3) download the

image to the G/S

Mean time to: 1) plan the

acquisitions, 2) uplink

the instructions, 3) take

the image, 4) download

the image to the G/S, 5)

post-process the data

Satellite groups

Estimated cost

Features

• Limited costs, already-existing assets exploited

• Increased situational awareness “for free”

• System flexible and scalable

• Scientific and commercial satellites only

• Integrates (cannot replace) in-situ S&R operations

• Agreements among operators are necessary

Navigation

&

Environment

Guidance

&

Control

Facilities

&

Experiments

Space Systems


Space Systems

Digital

Environment

reconstruction

Visual Odometry

for rover

Navigation

Hazard Maps

generation for landing

Planetary soil

sampling toolsSurface

Locomoton

Planetary ISRU

plants

Space platform

and orbital

robotics design


Spinning Target 8t

(Envisat)

Launch and capture

In orbit Robotics: Analysis & DesignActive Debris Removal

•Dynamics of complex flexible system in space

•Impact and wrapping, pulling

NUMERICAL SIMULATORS:

Design & Dynamics

Contact devices

Flexible systems=net+tether

In orbit Robotics: Analysis & DesignActive Debris Removal

•Stack flexible dynamics control during disposal

NUMERICAL SIMULATORS:

Design & Dynamics

Contact devices

Flexible systems=net+tether

Free dynamics + RCS

stabilisation

Large powered

maneuver

In orbit Robotics: Analysis & DesignActive Debris Removal Parabolic Flight

BREADBOARDING and ExperimentsSuccessful zero-g experiment: Novespace Flight in Bordeaux, June 12, 2015

Surface Hazard Maps autonomous generation to :

safe landing on unknown objects\surfaces

multi-level clustering algorithm supported by supervised learning (1024x124 tcom=3s)

In Orbit Robotics: Navigation &

Environment\Guidance Autonomous GNC for landing

Self-organizing

maps

Multilayer

Neural Network

Visual-based Navigation

•Single camera relative navigation.

•Image features are extracted and tracked

between subsequent frames.

•Data fusion together with complementary

sensors: Inertial Measurement Units

(IMU) and laser altimeters.

•On going.

In Orbit Robotics: Navigation & Environment\Guidance Autonomous GNC for landing

Comet 67P landing site-Hazard map

Moon – Lamror Q Crater

Backup landing region from NavCam –

Rosetta s\c

In Orbit Robotics: GNC for landing Facility setup

Moon – Facility architecture Moon DEM for the diorama and

rendering

Manufactured Diorama Visual results

Biomimetic Legged robot projects inspired by insects:

Prototype to

CTRNN Neural Network and distributed control

testing

Smart Materials for actuation

Surface Robotics: locomotion

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

5

10

15

20

25

30

35

40

45

T [Nm]

DP

[N

]

Drawbar pull as a function of torque with different eccentricity

e = 0

e = 0.8

Wheeled Rover Traversability

improvements

Sensitivity to wheel shape variation based

on Smart materials implementation

-0.1 -0.05 0 0.05 0.1-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

x [m]

z [m

]

Deformation from circular to elliptic wheel

increasing e

– Digital Environment Reconstruction: Stereo Vision System

– Motion State Reconstruction: Visual Odometry

– Path planning algorithms

z

mx

m

y

Displacement

And Rotation

55

Navigation & Environment: Surface mobility

Robotics: ControlHigh level control: autonomous reasoning

56

Deliberate

GoalActivity

Execute

Actioncommand

React

MonitoringBehaviour

Three layers architecture

Goal Manager

Recovey

Agent

Planner/Scheduler

Executer

Failure

Detection

Isolation

WorldActuatorsSensors

World

Environ.

GM

P/S

EX

Agent 1

FDI

R

HW1 M

W

Simulator

M

COM COM

HWn

GM

P/S

EX

M

Agent n

FDI

R

Logic Single Agent Multi-Agent

Applied to:

•Physical distribution scenarios: team of homogeneous\heterogeneous space vehicles

•Functional distribution scenarios: ground stations functionalities for data management

Surface Robotics: Analysis & DesignSampling mechanisms

SD2 Soil modeling DEM

Sampling tool design

Granular mechanics: simulation of sampling

Instant pusher

• Fast grab bucket

1,5s; 37g

Rotating stinger

• 1,5s; 7g

1,5s; 58g

3s; 38g

2014 Philae – Rosetta Lander – landed on comet

Churymov-Gerasimenko: PoliMi drill SD2 PISD2

Surface Robotics: Cometary lander - Philae

Electric power generation

simulator

Mechanical verification

Drill behavior in uncertain material and

microgravity conditions

Definition of optimal drilling strategies

Definition of contingency operations

Scientific use of SD2

Mission plans development

2014 Philae – Rosetta Lander – landed on comet

Churymov-Gerasimenko: PoliMi drill SD2 PI

SD2

Surface Robotics: Planetary sample collection

Current evolution

Drill for Moon caps: behavior in icy soil

Definition of optimal drilling strategies to

preserve volatiles

Energy exchange modelling and

validation

Subsurface composition and

physical parameters needed to

tune the tool and operations

design

Surface Robotics: Biomimetic Legged Robot

Until Now Planetary Exploration has been carried on with wheeled rover

Wheels have problems:Overcoming of large obstacles

Navigating on high slope

Low efficiency on rough terrain

Legs can solve those problem but introduce other:Mechanical configuration not passively stable

Higher power consumption (even at rest)

Low PL/BUS Mass Ratio

The Walking Motion of insects is usually characterized by:

Non-continuous contact with ground

Multiple DOFs should be coordinated and moved together

Intrinsic redundancy (required by passive stability)

Walking Motion Generation and Control is realized with a Decentralized approach (Reflex-based): The periodic motion is not imposed centrally but arises from the basic behaviors (reflexes) implemented at leg and joint level.

Legs CoordinationLegs Control

Joint Control

Decentralized: Bottom-up Approach

Centralized

Surface Robotics: Biomimetic Legged Robot

The controller is organized in two layers:

Low Level Velocity Joint control (RT embedded on robot HW)

High Level Motion Generation and control

– Single Leg Control (Dynamic CTRNN)

• Reflex included

– Leg Coordination (Fuzzy Logic Rules)

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

x [m]

z [m

]

Deformation from circular to circular wheel

increasing r

Surface Robotics: Variable shape Wheel

Solutions in planetary rovers to improve traversability performances:

Grousers

Flexible wheels

Variable shape wheels

Eccentricity variation (e) Radius variation (r)

-0.1 -0.05 0 0.05 0.1-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

x [m]

z [m

]

Deformation from circular to elliptic wheel

increasing e

Traversability is the robot’s ability to traverse soft soils or hard ground without loss of traction


Index of traversability is drawbar pull

DP = H - R

DP = 0

NO MOTION

DP > 0

MOTION

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.50

5

10

15

20

25

30

35

40

45

= 0°

= 5°

= 10°

= 15°

= 20°

T [Nm]

DP

[N

]

Drawbar pull as a function of torque with different slope angle

0 0.5 1 1.5 2 2.5 3 3.5 40

5

10

15

20

25

30

35

40

= 0°

= 5°

= 10°

= 15°

= 20°

T [Nm]

DP

[N

]

Drawbar pull as a function of torque with different slope angle

Circular wheel (e = 0) Elliptic wheel (e = 0.8)

≈14° ≈21°


0 1 2 3 4 5 6 7 8 9 10 110

5

10

15

20

25

30

35

40

45

50

55

r = 0.06m

r = 0.09m

r = 0.12m r = 0.15m

r = 0.18m

T [Nm]

DP

[N

]

Drawbar pull as a function of torque with different radius

Sampling Mechanisms: DEM

‣ Soil is modelled as a granular material: set of particles interacting with

contact or contactless forces

‣ At each integration step:

1. Set forces on particles from previous step

2. Detect collisions and update interactions

3. Solve interactions to apply forces

4. Integrate motion equations to study evolution

‣ Main advantages:

• Reduced cost for preliminary design: e.g., performance comparison between

different mechanisms to limit number of breadboards

• Provides requirements for system design (mechanical loads)

• Can be used to simulate behaviour in conditions that are costly or difficult to

reproduce in test campaigns (e.g., microgravity)

‣ The model is first calibrated to represent the

target soil


‣ Main phases:

• Soil specimen generation and calibration:

o Set soil properties (density, friction angle,

Young's modulus,…)

o Soil deposition, shaking, and scraping

• Sampling tool modelling

o Geometry

o Mechanical properties

• Kinematic trajectory generation

o Kinematic trajectory can be imposed to

reduce computational cost

o Presence of springs can be simulated with

triggers

o Tools dynamics can be included at

increased computational cost

• Simulation

‣ Study performed for Selex-ES under ESA Contract “Breadboard of a

Sampling Tool Mechanism for Low Gravity Bodies” (E915-003MS)


‣ Study performed for Selex-ES under ESA Contract “Breadboard of a

Sampling Tool Mechanism for Low Gravity Bodies” (E915-003MS)

spring

‣ Main phases:

• Soil specimen generation and calibration:

o Set soil properties (density, friction angle,

Young's modulus,…)

o Soil deposition, shaking, and scraping

• Sampling tool modelling

o Geometry

o Mechanical properties

• Kinematic trajectory generation

o Kinematic trajectory can be imposed to

reduce computational cost

o Presence of springs can be simulated with

triggers

o Tools dynamics can be included at

increased computational cost

• Simulation


‣ Preliminary results

1-g simulation 0-g simulation

Simulation Filling percentage Collected sample [g]

1-g 20.0 % 33.0

0-g 29.2 % 48.5


‣ Preliminary results

1-g simulation 0-g simulation

‣ DEM could serve other fields. E.g., simulation of landing on dusty soils:

• Estimate mechanical loads in different landing configurations and for different

landing mechanisms

• Study the dynamics of dust particles to estimate the effects on mechanisms or

the risk of contamination

Experimental Facilities

• Thermal Vacuum Chamber (-75° +200°, 10-6 mbar)

• Palamede:students’microsatellite implementation

• Friction free table

• Exploited for Internal and

external testing

• Predictive control to optimize

chamber performance

• 42,8x42,8x40 cm Earth

observation microsatellite

• p\l: CCD camera; Ptot=58W;

Mtot=35kg

• Educational and research goals

• 3x3m glass table

• GNC for proximity maneuvering

• robotics in microgravity conditions

Generalized Predictive Control: TVC tests

Model Predictive Control•Mathematical model of the System (predictor)•u(t) obtained with minimization of a cost function

•Recursive strategy (update of prediction at each step)

Identification (ARX Method)•Linear multistep predictor•Identification can be performed online or offline

s s p py k Tu k Bu k p Ay k p

k tk p sk h

:

:

sh

p

Prediction horizon

Order of the model

1

2

T T

s s s s s sJ k y k y k Q y k y k u k Ru k

1 k t

,u y

u

y

passato futuro

k p sk h

Predicted output

evaluation( )u k

Desired output


Performances WRT classical control

TRP on the component under test

Preliminary tests

– constant error (can be corrected)

– Regular control (lower value)

PID

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

58

59

60

61

T [

°C]

t [s]

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

2

4

6

8 VPID

[V

]

TTRPo

TTRP

VPID

GPC (SIMO)

TRP

TBASEPLAT

E

y

TRP

TBASEPLAT

E

1y2y

4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000

58

59

60

61

T [

°C]

t [s]

4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 140000

2

4

6

8

VR [

V]

TTRPo

TTRP

VR

8

5s

p

h

0.005

1dt s


Variation of TRP during test

New identification maintain performances

Without identification the controls fails.

Ph.D. on the application of Nonlinear GPC to attitude control of unkownobject (Uncooperative Targets, Debris Removal)

TRP2TRP1

( *)y per t t( *)y per t t

500 1000 1500 2000 2500 3000 3500 4000

37.5

38

38.5

39

39.5

40

40.5

41

T [

°C]

t [s]

500 1000 1500 2000 2500 3000 3500 40000

2

4

6

8

VR [

V]

ID

t = t*

TBPo

TBP

VR

TRP1 TRP2

New–Idendification

1000 1500 2000 2500 3000 3500 4000 4500 500036

37

38

39

40

41

T [

°C]

t [s]

1000 1500 2000 2500 3000 3500 4000 4500 50000

2

4

6

8

VR [

V]

t = t*

TTRPo

TTRP1

TTRP2

VR

NO

Identification

TRP1 TRP2

Feasibility studies

3 per year phase A studies on potential future space mission of ESA interest

•Europa Lander; Binary Asteroids deviation; Formation Flying at the magnetopause

•Manned mission to NEA; Fractioned satellite on Earth orbit; Phobos sample return

•Mission to Enceladus; Mission to Pluto-Caron; Active Debris Removal & in orbit servicing

•Lunar cold trap mission; Mission to Neptune; Mars Lagrangian point station

•Venus sample return; Pioneer Anomaly detection mission; Troposhere monitoring

•Jupiter Moons tour; large X-ray telescope; Earth Energy sources mapping

• Manned mission on the Moon; GPS on Mars; Vega Launcher upper stage

•In situ Resource Utilisation on Mars; interplanetary GPS;

department of aerospace science and technology · ‣part of the n/s arm of the northern cross...

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